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FloBab73 2026-10-27 08:22:27 +01:00
parent d6b2b5bfd3
commit 7db7e98181
5 changed files with 285 additions and 63 deletions
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2
pyproject.toml
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@ -8,7 +8,7 @@ dependencies = [
"jupyterlab>=4.6.4", "jupyterlab>=4.6.4",
"matplotlib>=3.11.2", "matplotlib>=3.11.2",
"numpy>=2.5.4", "numpy>=2.5.4",
"pandas>=3.0.6", "pandas==3.1.0",
"plotly>=7.1.0", "plotly>=7.1.0",
"scikit-learn>=1.9.1", "scikit-learn>=1.9.1",
] ]

70
uv.lock
View File

@ -1211,7 +1211,7 @@ requires-dist = [
{ name = "jupyterlab", specifier = ">=4.6.4" }, { name = "jupyterlab", specifier = ">=4.6.4" },
{ name = "matplotlib", specifier = ">=3.11.2" }, { name = "matplotlib", specifier = ">=3.11.2" },
{ name = "numpy", specifier = ">=2.5.4" }, { name = "numpy", specifier = ">=2.5.4" },
{ name = "pandas", specifier = ">=3.0.6" }, { name = "pandas", specifier = "==3.1.0" },
{ name = "plotly", specifier = ">=7.1.0" }, { name = "plotly", specifier = ">=7.1.0" },
{ name = "scikit-learn", specifier = ">=1.9.1" }, { name = "scikit-learn", specifier = ">=1.9.1" },
] ]
@ -1293,47 +1293,47 @@ wheels = [
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source = { registry = "https://pypi.org/simple" } source = { registry = "https://pypi.org/simple" }
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{ name = "python-dateutil" }, { name = "python-dateutil" },
{ name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" }, { name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" },
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40
Übung 4/SLN.py
View File

@ -1,11 +1,12 @@
import numpy as np
import nnwplot
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np
import nnwplot
def error_rate(Y, T): def error_rate(Y, T):
if Y.ndim == 1 or Y.shape[0] == 1: if Y.ndim == 1 or Y.shape[0] == 1:
errors = Y - T errors = Y != T
return errors.sum() / Y.size return errors.sum() / Y.size
else: # für mehrere Ausgaben in one-hot Kodierung: else: # für mehrere Ausgaben in one-hot Kodierung:
errors = Y.argmax(0) != T.argmax(0) errors = Y.argmax(0) != T.argmax(0)
@ -21,15 +22,9 @@ class SLN:
self._W = np.random.randn(dIn, cOut) / np.sqrt(dIn + 1) self._W = np.random.randn(dIn, cOut) / np.sqrt(dIn + 1)
def neuron(self, X): def neuron(self, X):
N = X.shape[1] net = self._W.T.dot(X) + self._b
net = np.zeros(N)
for n in range(0, N): return net >= 0
value = 0
for i in range(self.dIn):
value += X[i, n] * self._W[i][0]
net[n] += value + self._b[0][0]
return net
def delta_train(self, X, T, eta, maxIter, maxErrorRate): def delta_train(self, X, T, eta, maxIter, maxErrorRate):
N = X.shape[1] # Anzahl Trainingsdaten N = X.shape[1] # Anzahl Trainingsdaten
@ -37,22 +32,17 @@ class SLN:
for it in range(maxIter): for it in range(maxIter):
Y = self.neuron(X) # nur ein Aufruf Y = self.neuron(X) # nur ein Aufruf
err = error_rate(Y, T) err = error_rate(Y, T)
if (it % 10) == 0: # if (it % 10) == 0:
print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b)) print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b))
nnwplot.plotTwoFeatures(X, T, self.neuron) nnwplot.plotTwoFeatures(X, T, self.neuron)
plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht) plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
if err <= maxErrorRate: if err <= maxErrorRate:
break break
# update _W
for n in range(N): delta = T - Y
for j in range(self.dIn): self._W += X.dot(delta.T) * eta
weighted_error = err * X[j, n] self._b += delta.sum() * eta
to_learn = weighted_error * eta
#print("{:.2f} = {} * {} * {}".format(to_learn, err, X[j, n], eta))
self._W[j, 0] += to_learn
self._b[0][0] -= to_learn
# update _b
...
print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b)) print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b))
nnwplot.plotTwoFeatures(X, T, self.neuron) nnwplot.plotTwoFeatures(X, T, self.neuron)
plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht) plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)

4
Übung 4/nnwplot.py
View File

@ -16,7 +16,7 @@ def plotTwoFeatures(X,T,pred_func):
# determine canvas borders # determine canvas borders
mins = np.amin(X,1); mins = np.amin(X,1);
mins = mins - 0.1*np.abs(mins); mins = mins - np.where(mins == 0, 0.1, 0.1 * np.abs(mins))
maxs = np.amax(X,1); maxs = np.amax(X,1);
maxs = maxs + 0.1*maxs; maxs = maxs + 0.1*maxs;
@ -45,5 +45,5 @@ def plotTwoFeatures(X,T,pred_func):
# Plot the contour and training examples # Plot the contour and training examples
plt.contourf(xs, ys, Z, cmap=plt.cm.Spectral) plt.contourf(xs, ys, Z, cmap=plt.cm.Spectral)
plt.scatter(X[0,:], X[1,:], c=T, s=50, plt.scatter(X[0,:], X[1,:], c=T, s=50,
cmap=colors.ListedColormap(['orange', 'blue', 'green'])) cmap=colors.ListedColormap(['blue', 'green']))
plt.show() plt.show()

232
Übung 4/training.ipynb 100644
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